Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

A Mixed Geographically Weighted Regression (GWR) model is applied to explore the effects of shared mobility trips on taxi and public transit ridership at the macro-level. Several essential variables, including socioeconomic, transportation, network, and land use data, are set as the causal factors. The experiment is conducted using the smart card data, vehicle GPS trajectories, and vehicle order data collected in Shenzhen City, China. We show that the Mixed GWR outperforms the basic GWR in model fitting and capturing the unobserved heterogeneity. The spatial analysis reveals that bike-sharing addresses the “last-mile” and “first-mile” problems to bus and metro in the urban periphery. It substitutes the bus and taxis in short-distance journeys in the city center. However, the over-placement of bike-sharing in some regions limits the flexibility of bike-sharing connections to the metro. In the city center, ride-hailing fills the gaps in bus coverage and competes with the metro. In the peripheral areas, ride-hailing replaces buses and improves the accessibility to metro stations. The transportation policy increases the cooperation between ride-hailing and taxis citywide, although competitions in few regions need to be solved. The abovementioned results provide policy suggestions to optimize the allocation of local transportation resources.